Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

13.8K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.8K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

198
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
198
Sampling Plans01:23

Sampling Plans

789
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
789
Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

809
The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
809

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Pan-genome analysis and abiotic stress expression of the <i>SWEET</i> gene family in <i>Brassica napus</i>.

Frontiers in plant science·2026
Same author

Curvature-Induced Giant Second-Harmonic Generation in WS<sub>2</sub> Nanoscroll on a Metallic Film.

ACS nano·2026
Same author

SPIRAL: A probabilistic deep learning framework for Chinese liquor (Baijiu) classification via near-infrared hyperspectral imaging.

Food chemistry·2026
Same author

Multi-stage optoelectronic hybrid recognition of signed high-order orbital angular momentum modes.

Optics express·2026
Same author

Plasma Proteomic Dynamics Preceding Glaucoma Reveal a 15-Year Pre-Diagnostic Window: Causal Insights and Predictive Utility in 45,850 Participants.

Investigative ophthalmology & visual science·2026
Same author

Plant hormones and membrane transporters: integrating nutrient uptake, ion homeostasis, and stress responses through hormonal cross-talk.

Frontiers in plant science·2026

Related Experiment Video

Updated: Dec 16, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.7K

Model-based distributed node clustering and multi-speaker speech presence probability estimation in wireless acoustic

Yingke Zhao1, Jesper Kjær Nielsen2, Jingdong Chen3

  • 1Center of Intelligent Acoustics and Immersive Communications and School of Marine Science and Technology, Northwestern Polytechnical University, 127 Youyi West Road, Xi'an 710072, China.

The Journal of the Acoustical Society of America
|July 3, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel distributed method for estimating speech presence probability (SPP) in wireless acoustic sensor networks (WASNs). The approach enables robust multi-speaker detection without a central fusion system.

More Related Videos

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.9K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

990

Related Experiment Videos

Last Updated: Dec 16, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.7K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.9K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

990

Area of Science:

  • Signal Processing
  • Acoustic Sensor Networks
  • Machine Learning

Background:

  • Speech presence probability (SPP) is crucial for noise estimation and speech enhancement.
  • Existing SPP estimation methods are primarily single-channel or centralized multi-channel.
  • SPP estimation in wireless acoustic sensor networks (WASNs), especially with multiple speakers, presents significant challenges.

Purpose of the Study:

  • To develop a distributed model-based SPP estimation method for multi-speaker detection in WASNs.
  • To eliminate the need for a central fusion center in SPP estimation.
  • To enhance speech detection robustness in challenging acoustic environments.

Main Methods:

  • A distributed k-means clustering algorithm is employed to group sensor nodes into subnetworks for individual speaker detection.
  • Local estimation of speech and noise power spectral densities is performed at each node within subnetworks.
  • A distributed consensus method facilitates both distributed clustering and SPP estimation.

Main Results:

  • The proposed distributed clustering effectively assigns nodes to subnetworks based on noisy observations.
  • The distributed SPP estimator demonstrates robust speech detection capabilities across various noise conditions.
  • The method successfully addresses multi-speaker scenarios in WASNs without centralized processing.

Conclusions:

  • The developed distributed SPP estimation method is effective for multi-speaker detection in WASNs.
  • The approach offers a decentralized solution, overcoming limitations of centralized systems.
  • This work advances SPP estimation techniques for complex acoustic sensor network applications.